Remove 2020 Remove Data Pipeline Remove Data Science
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Five Interesting Data Engineering Projects

KDnuggets

As the role of the data engineer continues to grow in the field of data science, so are the many tools being developed to support wrangling all that data. Five of these tools are reviewed here (along with a few bonus tools) that you should pay attention to for your data pipeline work.

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The 2021 Executive Guide To Data Science and AI

Applied Data Science

This post is a bitesize walk-through of the 2021 Executive Guide to Data Science and AI  — a white paper packed with up-to-date advice for any CIO or CDO looking to deliver real value through data. Automation Automating data pipelines and models ➡️ 6. Team Building the right data science team is complex.

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Feature Platforms?—?A New Paradigm in Machine Learning Operations (MLOps)

IBM Data Science in Practice

VC Investment in AI firms rose from USD 3 billion in 2012 to close to USD 75 billion in 2020 This trend led to the proliferation of companies developing tools to address different pain points in the machine learning lifecycle. A feature platform should automatically process the data pipelines to calculate that feature.

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How The Explosive Growth Of Data Access Affects Your Engineer’s Team Efficiency

Smart Data Collective

In fact, you may have even heard about IDC’s new Global DataSphere Forecast, 2021-2025 , which projects that global data production and replication will expand at a compound annual growth rate of 23% during the projection period, reaching 181 zettabytes in 2025. zettabytes of data in 2020, a tenfold increase from 6.5

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AIOps vs. MLOps: Harnessing big data for “smarter” ITOPs

IBM Journey to AI blog

Wearable devices (such as fitness trackers, smart watches and smart rings) alone generated roughly 28 petabytes (28 billion megabytes) of data daily in 2020. And in 2024, global daily data generation surpassed 402 million terabytes (or 402 quintillion bytes). Massive, in fact.

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Pioneering computer vision: Aleksandr Timashov, ML developer

Dataconomy

In this interview, Aleksandr shares his unique experiences of leading groundbreaking projects in Computer Vision and Data Science at the Petronas global energy group (Malaysia). Please tell our readers about your background and how you got into Data Science and Machine Learning? Hello Aleksandr.

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Best 8 Data Version Control Tools for Machine Learning 2024

DagsHub

The following points illustrates some of the main reasons why data versioning is crucial to the success of any data science and machine learning project: Storage space One of the reasons of versioning data is to be able to keep track of multiple versions of the same data which obviously need to be stored as well.